Integer compression algorithms play an important role in columnoriented data systems. Previous research has shown that the vectorized implementation of these algorithms based on the Single Instruction Multiple Data (SIMD) parallel paradigm can multiply the compression as well as decompression speeds. While a scalar compression algorithm usually compresses a block of N consecutive integers, the state-of-the-art SIMD implementation scales the block size to k ∗ N with k as the number of elements which could be simultaneously processed in a SIMD register. However, this means that as the SIMD register size increases, the block of integer values for compression also grows, which can have a negative effect on the compression ratio. In this paper, we analyze this effect and present an idea for a novel general approach for the SIMD implementation of integer compression algorithms to overcome that effect. Our novel idea is to concurrently compress k different blocks of size N within SIMD registers. To show the applicability of our idea, we present initial evaluation results for a heavily used compression algorithm and show that our approach can lead to more responsible usage of main memory resources.